Large Language Model
Dissecting Chain-of-Thought: Compositionality through In-Context Filtering and Learning
Chain-of-thought (CoT) is a method that enables language models to handle complex reasoning tasks by decomposing them into simpler steps.
EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models
Our source code for our work is available here.
Estimating the Hallucination Rate of Generative AI Andrew Jesson Nicolas Beltran-V elez * Quentin Chu
In ICL, a conditional generative model (CGM) is prompted with a dataset and a prediction question and asked to generate a response.
BIGBIO: A Biomedical
BLUE (Biomedical Language Understanding Evaluation) is abenchmarkfor 10 datasetsrepresenting 5 tasks [34]. BLURB (Biomedical Language Understanding and Reasoning Benchmark) includes 13 datasetsand 7 tasks [19].
Delta-CoMe: Training-Free Delta-Compression with Mixed-Precision for Large Language Models Bowen Ping
Recent studies suggest decomposing a fine-tuned LLM into a base model and corresponding delta weights, which are then compressed using low-rank or low-bit approaches to reduce costs. In this work, we observe that existing low-rank and low-bit compression methods can significantly harm the model performance for task-specific fine-tuned LLMs (e.g., WizardMath for math problems).